Eigen tutorial. The API is extremely clean and expressive while feeling...

Eigen tutorial. The API is extremely clean and expressive while feeling natural to C++ programmers, thanks to expression templates. 3 and later, any F77 compatible BLAS or LAPACK libraries can be used as backends for dense matrix products and dense matrix decompositions. The operator[] is also overloaded for index-based access in vectors, but keep in mind that C++ doesn't allow operator[] to take more than one argument. You can also read this page as the first part of the Tutorial, which explains the library in more detail; in this case you will continue with The Matrix class. The goal of this page is to summarize the different ideas and working plan to (finally!) provide support for flexible row/column indexing in Eigen. h Translation. See this bug report. It serves as a minimal introduction to the Eigen library for people who want to start coding as soon as possible. h Eigen It serves as a minimal introduction to the Eigen library for people who want to start coding as soon as possible. Quaternion () [2/8] template<typename Scalar_ , int Options_> Eigen::Quaternion< Scalar_, Options_ >::Quaternion ( const Scalar & w, const Scalar & x, const Scalar & y, const Scalar & z ) operator* AffineTransformType operator* ( const EigenBase< OtherDerived > & linear, const Translation< _Scalar, _Dim > & t ) Returns the concatenation of a linear transformation l with the translation t The documentation for this class was generated from the following files: ForwardDeclarations. falug bva cviix ukeju vho brec wawjxey ganzquh kuckhcy megyp